NanoCMSer: a consensus molecular subtype stratification tool for fresh‐frozen and paraffin‐embedded colorectal cancer samples
Colorectal cancer (CRC) is a significant contributor to cancer‐related mortality, emphasizing the need for advanced biomarkers to guide treatment. As part of an international consortium, we previously categorized CRCs into four consensus molecular subtypes (CMS1‐CMS4), showing promise for outcome pr...
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| Format: | Article |
| Language: | English |
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Wiley
2025-05-01
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| Series: | Molecular Oncology |
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| Online Access: | https://doi.org/10.1002/1878-0261.13781 |
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| author | Arezo Torang Simone van deWeerd Veerle Lammers Sander vanHooff Inge van denBerg Saskia van denBergh Miriam Koopman Jan N. IJzermans Jeanine M. L. Roodhart Jan Koster Jan Paul Medema |
| author_facet | Arezo Torang Simone van deWeerd Veerle Lammers Sander vanHooff Inge van denBerg Saskia van denBergh Miriam Koopman Jan N. IJzermans Jeanine M. L. Roodhart Jan Koster Jan Paul Medema |
| author_sort | Arezo Torang |
| collection | DOAJ |
| description | Colorectal cancer (CRC) is a significant contributor to cancer‐related mortality, emphasizing the need for advanced biomarkers to guide treatment. As part of an international consortium, we previously categorized CRCs into four consensus molecular subtypes (CMS1‐CMS4), showing promise for outcome prediction. To facilitate clinical integration of CMS classification in settings where formalin‐fixed paraffin‐embedded (FFPE) samples are routinely used, we developed NanoCMSer, a NanoString‐based CMS classifier using 55 genes. NanoCMSer achieved high accuracy rates, with 95% for fresh‐frozen samples from the MATCH cohort and 92% for FFPE samples from the CODE cohort, marking the highest reported accuracy for FFPE tissues to date. Additionally, it demonstrated 96% accuracy across a comprehensive collection of 23 RNAseq‐based datasets, compiled in this study, surpassing the performance of existing models. Classifying with only 55 genes, the CMS predictions were still biologically relevant, recognizing CMS‐specific biology upon enrichment analysis. Additionally, we observed substantial differences in recurrence‐free survival curves when comparing CMS2/3 patients in stage III versus II. Probability of recurrence after 5 years increased by 21% in CMS2 and 31% in CMS3 for patients in stage III, whereas this difference was less pronounced for CMS1 and CMS4, with 11% and 10%, respectively. We posit NanoCMSer as a robust tool for subtyping CRCs for both tumor biology and clinical practice, accessible via nanocmser r package (https://github.com/LEXORlab/NanoCMSer) and Shinyapp (https://atorang.shinyapps.io/NanoCMSer). |
| format | Article |
| id | doaj-art-be9abbe392624bbfad8141bfa8a1b7c8 |
| institution | OA Journals |
| issn | 1574-7891 1878-0261 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Wiley |
| record_format | Article |
| series | Molecular Oncology |
| spelling | doaj-art-be9abbe392624bbfad8141bfa8a1b7c82025-08-20T01:50:39ZengWileyMolecular Oncology1574-78911878-02612025-05-011951332134610.1002/1878-0261.13781NanoCMSer: a consensus molecular subtype stratification tool for fresh‐frozen and paraffin‐embedded colorectal cancer samplesArezo Torang0Simone van deWeerd1Veerle Lammers2Sander vanHooff3Inge van denBerg4Saskia van denBergh5Miriam Koopman6Jan N. IJzermans7Jeanine M. L. Roodhart8Jan Koster9Jan Paul Medema10Amsterdam UMC, Center for Experimental and Molecular Medicine, Cancer Center Amsterdam University of Amsterdam The NetherlandsAmsterdam UMC, Center for Experimental and Molecular Medicine, Cancer Center Amsterdam University of Amsterdam The NetherlandsAmsterdam UMC, Center for Experimental and Molecular Medicine, Cancer Center Amsterdam University of Amsterdam The NetherlandsAmsterdam UMC, Center for Experimental and Molecular Medicine, Cancer Center Amsterdam University of Amsterdam The NetherlandsDepartment of Surgery, Erasmus MC University Medical Center Rotterdam The NetherlandsAmsterdam UMC, Center for Experimental and Molecular Medicine, Cancer Center Amsterdam University of Amsterdam The NetherlandsDepartment of Medical Oncology, University Medical Center Utrecht Utrecht University The NetherlandsDepartment of Surgery, Erasmus MC University Medical Center Rotterdam The NetherlandsDepartment of Medical Oncology, University Medical Center Utrecht Utrecht University The NetherlandsAmsterdam UMC, Center for Experimental and Molecular Medicine, Cancer Center Amsterdam University of Amsterdam The NetherlandsAmsterdam UMC, Center for Experimental and Molecular Medicine, Cancer Center Amsterdam University of Amsterdam The NetherlandsColorectal cancer (CRC) is a significant contributor to cancer‐related mortality, emphasizing the need for advanced biomarkers to guide treatment. As part of an international consortium, we previously categorized CRCs into four consensus molecular subtypes (CMS1‐CMS4), showing promise for outcome prediction. To facilitate clinical integration of CMS classification in settings where formalin‐fixed paraffin‐embedded (FFPE) samples are routinely used, we developed NanoCMSer, a NanoString‐based CMS classifier using 55 genes. NanoCMSer achieved high accuracy rates, with 95% for fresh‐frozen samples from the MATCH cohort and 92% for FFPE samples from the CODE cohort, marking the highest reported accuracy for FFPE tissues to date. Additionally, it demonstrated 96% accuracy across a comprehensive collection of 23 RNAseq‐based datasets, compiled in this study, surpassing the performance of existing models. Classifying with only 55 genes, the CMS predictions were still biologically relevant, recognizing CMS‐specific biology upon enrichment analysis. Additionally, we observed substantial differences in recurrence‐free survival curves when comparing CMS2/3 patients in stage III versus II. Probability of recurrence after 5 years increased by 21% in CMS2 and 31% in CMS3 for patients in stage III, whereas this difference was less pronounced for CMS1 and CMS4, with 11% and 10%, respectively. We posit NanoCMSer as a robust tool for subtyping CRCs for both tumor biology and clinical practice, accessible via nanocmser r package (https://github.com/LEXORlab/NanoCMSer) and Shinyapp (https://atorang.shinyapps.io/NanoCMSer).https://doi.org/10.1002/1878-0261.13781colorectal cancerconsensus molecular subtypesmachine learningNanoStringprognosis biomarker |
| spellingShingle | Arezo Torang Simone van deWeerd Veerle Lammers Sander vanHooff Inge van denBerg Saskia van denBergh Miriam Koopman Jan N. IJzermans Jeanine M. L. Roodhart Jan Koster Jan Paul Medema NanoCMSer: a consensus molecular subtype stratification tool for fresh‐frozen and paraffin‐embedded colorectal cancer samples Molecular Oncology colorectal cancer consensus molecular subtypes machine learning NanoString prognosis biomarker |
| title | NanoCMSer: a consensus molecular subtype stratification tool for fresh‐frozen and paraffin‐embedded colorectal cancer samples |
| title_full | NanoCMSer: a consensus molecular subtype stratification tool for fresh‐frozen and paraffin‐embedded colorectal cancer samples |
| title_fullStr | NanoCMSer: a consensus molecular subtype stratification tool for fresh‐frozen and paraffin‐embedded colorectal cancer samples |
| title_full_unstemmed | NanoCMSer: a consensus molecular subtype stratification tool for fresh‐frozen and paraffin‐embedded colorectal cancer samples |
| title_short | NanoCMSer: a consensus molecular subtype stratification tool for fresh‐frozen and paraffin‐embedded colorectal cancer samples |
| title_sort | nanocmser a consensus molecular subtype stratification tool for fresh frozen and paraffin embedded colorectal cancer samples |
| topic | colorectal cancer consensus molecular subtypes machine learning NanoString prognosis biomarker |
| url | https://doi.org/10.1002/1878-0261.13781 |
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